statistical analysis of m/eeg sensor- and source-level data jason taylor
DESCRIPTION
Practical aspects of…. Statistical Analysis of M/EEG Sensor- and Source-Level Data Jason Taylor MRC Cognition and Brain Sciences Unit (CBU) Cambridge Centre for Ageing and Neuroscience (CamCAN) Cambridge, UK [email protected] | http://imaging.mrc-cbu.cam.ac.uk (wiki) - PowerPoint PPT PresentationTRANSCRIPT
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Statistical Analysis of M/EEG Sensor- and Source-Level Data
Jason Taylor MRC Cognition and Brain Sciences Unit (CBU)
Cambridge Centre for Ageing and Neuroscience (CamCAN)
Cambridge, UK
[email protected] | http://imaging.mrc-cbu.cam.ac.uk (wiki)
09 May 2011 | London | Thanks to Rik Henson and Dan Wakeman @ CBU
Practical aspects of…
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The SPM approach to M/EEG Statistics:
A mass-univariate statistical approach to inference regarding effects in space/time/frequency (using replications across trials or subjects).
• In Sensor Space:
2D Time-Frequency
3D Topography-by-Time
• In Source Space:
3D Time-Frequency contrast images
SPM vs SnPM vs PPM vs FDR
Other issues & Future directions
Overview
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The Multiple Comparison Problem
Random Field Theory (RFT) is a method for correcting for multiple statistical comparisons with N-dimensional spaces (for parametric statistics, e.g., Z-, T-, F- statistics).
Note these comparisons are often made implicitly, e.g., by viewing (“eye-balling”) data before selecting a time-window or set of channels for statistical analysis! RFT correction depends on the search volume.
-> When is there an effect in time, e.g., GFP (1D)?
-> When/What frequency is there an effect in time and frequency (2D)?
-> When/Where is there an effect in sensor topography space and time (3D)?
-> When/Where is there an effect in source space and time (4-ish D)?
Worsley Et Al (1996). Human Brain Mapping, 4:58-73
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CTF Multimodal Faces Dataset | SPM8 Manual (Rik Henson)
CTF MEG & EEG Data:128 EEG275 MEG3T fMRI (with nulls)1mm3 sMRI
2 sessions
~160 face trials and ~160 scrambled trials per session
Group: N=12 subjects, as in Henson et al, 2009 a,b,c
Chapter 33 SPM8 Manual
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Neuromag Faces Dataset | Dan Wakeman & Rik Henson @ CBU
Biomag 2010 Award-Winning Dataset!
Neuromag MEG & EEG Data:70 EEG102 Magnetometers204 Planar GradiometersH&VEOGECG1mm3 sMRI
6 sessions
faces & scrambled-face images (480 trials)
Group: N=18
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Neuromag Lexical Decision Dataset | Taylor & Henson @ CBU
Taylor & Henson, submitted
Neuromag MEG Data:102 Magnetometers204 Planar GradiometersH&VEOGECG1mm3 sMRI
4 sessions
words & pseudowords presented visually (480 trials)
Group: N=18
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Fac
esS
cram
ble
d
Faces > Scrambled
Where is an effect in time-frequency space?
Kilner et al., 2005, Nsci LettersCTF Multimodal Faces
Single MEG channelMean over trials of Morlet wavelet projection (i.e., induced + evoked)-Write t x f image per trial-Compute SPM, RFT correct
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Fac
es >
Scr
amb
led
Where is an effect in time-frequency space?
Neuromag Faces
EEG MEG (Mag)
100-220ms8-18Hz
Fre
q (H
z)
6
9
0
-500 +1000 Time (ms)
Fre
q (H
z)
6
9
0
-500 +1000 Time (ms)
Single channel-Effect over subjects
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Where is an effect in sensor-time space?
Topography-x-Time Image (EEG)
Stats (F): Faces vs Scrambled(single subject, over trials)
CTF Multimodal Faces
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GLM: Removing variance due to confounds
Each trial
Each trial-type (6)
beta_00* images reflect mean (adjusted) 3D topo-time volume for each condition
Confounds (4)
Henson et al., 2008, NImage
W/in Subject(1st-level) model
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Without transformation to Device Space
With transformation to Device Space
Where is an effect in sensor-time space?
Analysis over subjects (2nd Level)NOTE for MEG: Complicated by variability in head-positionSOLUTION: Virtual transformation to same position in sessions, subjects
Taylor & Henson (2008) BiomagNeuromag Lexical Decision
Stats over 18 subjects, RMS of planar gradiometers
*Improved* (i.e., more blobs, larger T values) w/ transform
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Where is an effect in source space (3D)?
Note sparseness of MSP inversions….
Analysis Mask
Source Analysis over N=12 subjects, MEG: 102 mags, MSP, evoked, RMS of source energy, smoothed 12-mm kernel
STEPS:
Estimate evoked/induced energy (RMS) at each dipole for a certain time-frequency window of interest
(e.g., 100-220ms, 8-18 Hz)
For each condition (Faces, Scrambled)
Smooth along 2D surface
Write data to 3D image (in MNI space)
Smooth by 3D Gaussian
Henson et al., 2007, NImage
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Where is an effect in source space (3D)?
MEG E+MEGA EEG
Neuromag Faces
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Where is an effect in source space (3D)?
Sensors
Over Participants
Over Voxels
Mags Grads
Classical SPM approach
Caveats:
-Inverse operator induces long-range error correlations (e.g., similar gain vectors from non-adjacent dipoles with similar orientation), making RFT conservative
-Need a cortical mask, else activity ‘smoothed’ outside
-Distributions over subjects may not be Gaussian (e.g., if sparse, as in MSP)
(Taylor & Henson, submitted; Biomag 2010)
Neuromag Lexical Decision
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Where is an effect in source space (3D)?
Sources (fused sensors)
Over Participants
Over Voxels
MSP IID
Classical SPM approach
Caveats:
-Inverse operator induces long-range error correlations (e.g., similar gain vectors from non-adjacent dipoles with similar orientation), making RFT conservative
-Need a cortical mask, else activity ‘smoothed’ outside
-Distributions over subjects may not be Gaussian (e.g., if sparse, as in MSP)
(Taylor & Henson, submitted; Biomag 2010)
Neuromag Lexical Decision
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Where is an effect in source space (3D)?
p<.05 FWENon-Parametric Approach (SnPM)
Robust to non-Gaussian distributions
Less conservative than RFT when dfs<20
Caveats:
No idea of effect size (e.g., for power, future expts)
Exchangeability difficult for more complex designs
(Taylor & Henson, Biomag 2010)
SnPM Toolbox by Holmes & Nichols:http://go.warwick.ac.uk/tenichols/software/snpm/
CTF Multimodal Faces
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Where is an effect in source space (3D)?
p>.95 (γ>1SD)Posterior Probability Maps (PPMs)
Bayesian Inference
No need for RFT (no MCP)
Threshold on posterior probabilty of an effect greater than some size
Can show effect size after thresholding
Caveats:
Assume Gaussian distribution (e.g., of mean over voxels), sometimes not met (ok for IID…?)
(Taylor & Henson, submitted; Biomag 2010)
CTF Multimodal Faces
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Where is an effect in source space (3D)?
Topological FDR correction
Choose an uncorrected threshold (e.g., p<.001) to define topologial features (e.g., peak and cluster size)
Topological FDR actually produces higher corrected p-values (i.e., fewer suprathreshol voxels) than FWE in the data used here
Caveat:
If sources are constrained to a gray matter cortical surface, are topological features as meaningful?
(Taylor & Henson, Biomag 2010)
SPM p<.001 unc
CTF Multimodal Faces
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Taylor & Henson, submittedNeuromag Lexical Decision
Condition x Time-windowInteractions
Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear.
We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses.
* high-dimensional distance between topography vectors
* cut the cluster tree where the solution is stable over longest range of distances
Where and When so effects emerge/disappear in source space (3D)?
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Taylor & Henson, submittedNeuromag Lexical Decision
Condition x Time-windowInteractions
Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear.
We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses.
* estimate sources in each sub-time-window
* submit to GLM with conditions & time-windows as factors (here: Cond effects per TWin)
Where and When so effects emerge/disappear in source space (3D)?
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Taylor & Henson, submittedNeuromag Lexical Decision
Condition x Time-windowInteractions
Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear.
We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses.
* estimate sources in each sub-time-window
* submit to GLM with conditions & time-windows as factors (here: Cond x TW interactions)
Where and When so effects emerge/disappear in source space (3D)?
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Future Directions
• Extend RFT to 2D cortical surfaces (+1D time)? (e.g., Pantazis et al., 2005, NImage)
• Novel approaches to the multiple comparison problem (e.g., 'unique extrema' in sensors: Barnes et al., 2011, NImage)
• Go multivariate? To identify linear combinations of spatial (sensor or
source) effects in time and space(e.g., Carbonnell, 2004, NImage)
To detect spatiotemporal patterns in 3D images(e.g., Duzel, 2004, NImage; Kherif, 2003, NImage)
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- The End -
Thanks!